# Labeling individual points in a scatter plot

Dear Knimers,
I have a situation very similar to the posts at Labeling individual points or clusters on a scatterplot?.
I don’t know how to use R – nor have available time for learning it by now – for labeling individual points in a scatter plot, and it would make sense to visually identify some points (both key points or minor outliers) in my graph, depicted below. Besides, the data table is also attached.

Munics_x_PoptTotMunic_x_CountCases_for_ScatterPlotLabelling.xlsx (15.5 KB).
Another (not so little) aspect of my analysis is that I must plot the best line passing closest to these points. Using the node Linear Correlation, I got this correlation value:

, which seems to be consistent enough for a visualization. What node should I use for such representation? I tried a few options, but got no satisfactory result.
Can someone help me?

Hi @rogerius1st ,

Welcome to the Forum!

1. labelling individual point: please check this example workflow. Here the Generic JavaScript View node was used (you mentioned that you would like to avoid R, in this example you can fit the script in JavaScript code to your specific dataset. It is true that in this solution you can read the “label” of the datapoint if you mouse over it. (See the attached pic, with the original dataset, not with yours.)

1. For fitting the best fit straight line (R-squared) please find this post as a reference. (See the attached picture with your dataset.)

From the 2D/3D Scatterplot node description: “To view supplemental information for an individual datapoint, right click on the point and a pop up window will appear. You can choose what is displayed here by selecting a column from the ‘label’ drop down, or alternatively a selection of columns which is set up via the node dialog.”

With this second solution you can get data point labelling, and best fitting line.

Please note that the R-squared line and the correlation are two different measures in statistical analyses.

Hope this helps!

Regards,
Dora

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